Beyond Trivial Edges: A Fractional Approach to Cohesive Subgraph Detection in Hypergraphs

TL;DR

This paper introduces the $(k,g,p)$-core model for hypergraph analysis, improving the detection of meaningful cohesive subgraphs by accounting for hyperedge importance, and presents algorithms that significantly enhance computational efficiency.

Contribution

The paper proposes a novel $(k,g,p)$-core model that refines hypergraph cohesive subgraph detection by incorporating hyperedge importance, along with efficient pruning algorithms.

Findings

Reduces costly operations by 51.9% on real datasets

Enhances accuracy of subgraph detection in hypergraphs

Demonstrates effectiveness through extensive experiments

Abstract

Hypergraphs serve as a powerful tool for modeling complex relationships across domains like social networks, transactions, and recommendation systems. The (k,g)-core model effectively identifies cohesive subgraphs by assessing internal connections and co-occurrence patterns, but it is susceptible to inflated cohesiveness due to trivial hyperedges. To address this, we propose the $(k,g,p)$-core model, which incorporates the relative importance of hyperedges for more accurate subgraph detection. We develop both Na\"ive and Advanced pruning algorithms, demonstrating through extensive experiments that our approach reduces the execution frequency of costly operations by 51.9% on real-world datasets.